System, method and computer program for varying affiliate position displayed by intermediary

ABSTRACT

Endogenous and exogenous variables associated with an item for sale by an affiliate may be displayed to a user of an affiliate decision-making tool. In response to the user selecting one of the endogenous variables, the affiliate decision-making tool may compute a number of introductions, a number of leads, and a number of sales for each of a plurality of possible values of the endogenous variable. The computation may be done utilizing a display position algorithm. A visualization of effects of setting the endogenous variable at different levels may be presented. The user may interact with the display position algorithm to vary one or more of the plurality of possible values of the endogenous variable such that the affiliate is eligible or disqualified to be displayed by an intermediary in response to a search for the item by a visitor of a network site owned and operated by the intermediary.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims a benefit of priorityunder 35 U.S.C. § 120 of the filing date of U.S. patent application Ser.No. 16/670,448, filed Oct. 31, 2019, entitled “SYSTEM, METHOD ANDCOMPUTER PROGRAM FOR VARYING AFFILIATE POSITION DISPLAYED BYINTERMEDIARY,” issued as U.S. Pat. No. 11,132,702, which is acontinuation of and claims a benefit of priority under 35 U.S.C. § 120of the filing date of U.S. patent application Ser. No. 14/604,014, filedJan. 23, 2015, entitled “SYSTEM, METHOD AND COMPUTER PROGRAM FOR VARYINGAFFILIATE POSITION DISPLAYED BY INTERMEDIARY,” issued as U.S. Pat. No.10,482,485, which is a continuation of and claims a benefit of priorityunder 35 U.S.C. § 120 of the filing date of U.S. patent application Ser.No. 13/891,835, filed May 10, 2013, entitled “SYSTEM, METHOD ANDCOMPUTER PROGRAM FOR VARYING AFFILIATE POSITION DISPLAYED BYINTERMEDIARY,” which claims a benefit of priority under 35 U.S.C. §119(e) from U.S. Provisional Application No. 61/646,075, filed May 11,2012, entitled “SYSTEM, METHOD AND COMPUTER PROGRAM FOR VARYING DISPLAYPOSITION IN AN ONLINE MARKET WITH KNOWN DEMAND,” all of which are fullyincorporated herein by reference.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material towhich a claim for copyright is made. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but reserves all other copyright rightswhatsoever.

TECHNICAL FIELD

This disclosure relates generally to ecommerce and online markets. Moreparticularly, this disclosure relates to an innovative methodology andsystem and computer program implementing the methodology that enables anaffiliate of an intermediary ecommerce company to determine how variousfactors may be modified in order to change the position of the affiliateand/or an item associated therewith being displayed to consumers in anonline market hosted by the intermediary ecommerce company.

BACKGROUND

Consumers are becoming savvier. This is especially true when access tothe Internet is readily available and research on a product or servicecan be easily accomplished online. Various sites on the Internet havemade it easy for consumers to search for products and services alikebefore making a purchase. Depending upon the capabilities of the networksites, consumers searching for certain products or services may bepresented with various types of information. For example, a consumer maysearch for a product or service via a portal or search site. In responseto the consumer's request, the search site may display a list ofhyperlinks corresponding to sellers that offer the requested product orservice. When the consumer clicks on a hyperlink corresponding to one ofthe sellers, the consumer's browser is directed to the consumer-selectedseller's website. The consumer-selected seller may have the requestedproduct or service available for purchase through its website or at aphysical location near the consumer.

In this case, the search site can be seen as an intermediary between theconsumer and the consumer-selected seller. However, theconsumer-selected seller may not be affiliated with the search site and,unlike advertisers that pay the search site to display ads, directingthe consumer to the consumer-selected seller may not yield financialbenefits to the search site. On the other hand, the consumer-selectedseller may have no control as to how it is ranked on the list providedby the search site in response to the consumer's search query on aparticular product or service.

SUMMARY OF THE DISCLOSURE

With the increasing popularity of consumers doing online searches forcandidate sales outlets (also referred to herein as vendors, sellers,dealers, etc.) before making purchases, there is an increasingopportunity to yield certain financial benefits for an intermediaryecommerce company by directing its website visitors toward affiliatedsales outlets (which is interchangeably referred to herein as“affiliates”).

For example, a consumer may initiated a search for a good or service ata network site owned and operated by an intermediary ecommerce company.When an eligible outlet is competing against others to sell the good orservice to the consumer, it is desirable that an affiliate's outlet beincluded in the search result provided by the intermediary ecommercecompany to the consumer and the online presentation thereof. It isfurther desirable that not only an affiliate's outlet be included in theonline presentation, but also be placed at a favorable positiondisplayed to the consumer. One reason is that, if chosen for inclusion,the order in which the eligible outlets are presented (display position)often is positively correlated to the probability of sale. In somepresentations, the outlet at the top of the display (in the firstposition) may have a higher probability of actually selling the good orservice than other outlets displayed.

This disclosure describes a methodology and a useful decision-makingtool implementing same that enable a business customer or affiliate ofan intermediary such as an intermediate ecommerce company to determinehow various business decisions and performance (i.e., setting of itemprices, inventory management—including pricing and accessibility ofsubstitutes, relative velocity of recent sales, customer satisfactionrates, etc.) may be modified in order to be included in the displayand/or to change their position displayed to a consumer visiting anonline market owned and operated by the intermediary. Using variouslevers to achieve a desired display position can allow an affiliate tohave more control over the amount of times and/or how he is presented toa customer, thereby influencing gross margin (the difference between thesales price and the cost of the item).

In some embodiments, a system implementing a methodology disclosedherein may include an affiliate decision-making tool executing on one ormore server machines owned and operated by an intermediary. Theaffiliate decision-making tool may be configured to, among others, causeendogenous and exogenous variables to be displayed on a client machinecommunicatively connected to the one or more server machines. Theseendogenous and exogenous variables may be associated with an item forsale by an affiliate owning and operating the client machine. As anexample, the item may represent a specific vehicle configuration.

In response to a user at the client machine selecting one of theendogenous variables, the system may compute a number of introductions,a number of leads, and a number of sales for each of a plurality ofpossible values of the endogenous variable. The system may cause avisualization on the client machine to show effects of setting the firstendogenous variable at different levels of the number of introductions,the number of leads, and the number of sales.

In some embodiments, the affiliate decision-making tool may implement adisplay position algorithm configured to compute an expected revenue forthe intermediary and a display position for the affiliate. The displayposition algorithm may also compute an expected revenue for theaffiliate. The affiliate decision-making tool may allow the user at theclient machine to interact with the display position algorithm to varyone or more of the plurality of possible values of the endogenousvariable such that the affiliate is eligible or disqualified to bedisplayed by the intermediary in response to a search for the item by avisitor of a network site owned and operated by the intermediary.

In some embodiments, the display position algorithm can be configured tocompute, for the affiliate, an adjustment to the endogenous variablesuch that a display position for the affiliate is not more than amaximum number of display positions set by the intermediary, therebyensuring that the affiliate is displayed by the intermediary in responseto a search for the item.

In some embodiments, the display position algorithm can be configured tocompute, for the affiliate, an adjustment to the endogenous variablesuch that a display position for the affiliate is at a specific positionset by the affiliate. The specific position can be within a maximumnumber of display positions set by the intermediary for responding to asearch for the item.

In some embodiments, a computer program product may include at least onenon-transitory computer-readable medium storing instructionstranslatable by at least one processor to implement a methodologydisclosed herein. Various implementations may be possible.

These, and other, aspects of the invention will be better appreciatedand understood when considered in conjunction with the followingdescription and the accompanying drawings. The following description,while indicating various embodiments of the invention and numerousspecific details thereof, is given by way of illustration and not oflimitation. Many substitutions, modifications, additions orrearrangements may be made within the scope of the invention, and theinvention includes all such substitutions, modifications, additions orrearrangements.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings accompanying and forming part of this specification areincluded to depict certain aspects of the invention. A clearerimpression of the invention, and of the components and operation ofsystems provided with the invention, will become more readily apparentby referring to the exemplary, and therefore non-limiting, embodimentsillustrated in the drawings, wherein identical reference numeralsdesignate the same components. Note that the features illustrated in thedrawings are not necessarily drawn to scale.

FIG. 1 depicts of one embodiment of a topology including a vehicle datasystem.

FIG. 2 depicts a diagrammatic representation of one embodiment of asystem implementing a methodology disclosed herein.

FIG. 3 depicts a diagrammatic representation of an example data flowaccording to one embodiment of a method for varying an affiliateposition displayed by an intermediary.

FIG. 4 depicts a diagrammatic representation of an example data flowaccording to one embodiment of a dealer scoring algorithm.

FIG. 5 depicts a flow diagram illustrating one embodiment of a methodfor ensuring an introduction utilizing an affiliate decision-makingtool.

FIG. 6 depicts a flow diagram illustrating one embodiment of a methodfor varying display position to a specific rank utilizing an affiliatedecision-making tool.

FIG. 7A depicts example variables utilized in one embodiment of amethodology disclosed herein.

FIG. 7B depicts possible values of an example variable utilized in oneembodiment of a methodology disclosed herein.

FIG. 7C depicts a plot diagram illustrating effects of setting anexample variable utilized in one embodiment of a methodology disclosedherein.

FIG. 8 illustrates by example how price changes may, according to oneembodiment disclosed herein, affect whether an affiliate is displayedand, if displayed, in what position.

FIG. 9 depicts example aggregate prices information of certain modelsand trims for an affiliate according to one embodiment disclosed herein.

FIG. 10 depicts a plot diagram illustrating an example relationshipbetween predicted leads and item price according to one embodimentdisclosed herein.

FIG. 11 depicts an example interface showing a portion of a vehicle datasystem implementing one embodiment of a methodology disclosed herein.

FIG. 12 depicts an example interface showing a portion of a vehicle datasystem implementing one embodiment of a methodology disclosed herein.

DETAILED DESCRIPTION

The disclosure and various features and advantageous details thereof areexplained more fully with reference to the exemplary, and thereforenon-limiting, embodiments illustrated in the accompanying drawings anddetailed in the following description. It should be understood, however,that the detailed description and the specific examples, whileindicating the preferred embodiments, are given by way of illustrationonly and not by way of limitation. Descriptions of known programmingtechniques, computer software, hardware, operating platforms andprotocols may be omitted so as not to unnecessarily obscure thedisclosure in detail. Various substitutions, modifications, additionsand/or rearrangements within the spirit and/or scope of the underlyinginventive concept will become apparent to those skilled in the art fromthis disclosure.

Before discussing embodiments of the invention, a topology whereembodiments disclosed herein can be implemented is described withreference to FIG. 1 . As one skilled in the art can appreciate, theexemplary architecture shown and described herein with respect to FIG. 1is meant to be illustrative and not limiting.

FIG. 1 depicts one embodiment of a topology which may be used toimplement embodiments of the systems and methods disclosed herein.Topology 100 comprises a set of entities including vehicle data system120 (also referred to herein as the TrueCar system) which is coupledthrough network 170 to computing devices 110 (e.g., computer systems,personal data assistants, kiosks, dedicated terminals, mobiletelephones, smart phones, etc.), and one or more computing devices atinventory companies 140, original equipment manufacturers (OEM) 150,sales data companies 160, financial institutions 182, externalinformation sources 184, departments of motor vehicles (DMV) 180 and oneor more associated point of sale locations, in this embodiment, cardealers 130. Computing devices 110 may be used by consumers whileconducting a search for consumer goods and/or services, such asautomobiles. Network 170 may be or include, for example, a wireless orwired communication network such as the Internet or wide area network(WAN), publicly switched telephone network (PTSN) or any other type ofelectronic or non-electronic communication link such as mail, courierservices or the like.

Vehicle data system 120 may comprise one or more computer systems withcentral processing units executing instructions embodied on one or morecomputer-readable media where the instructions are configured to performat least some of the functionality associated with embodiments disclosedherein. These applications may include a vehicle data application 190comprising one or more applications (instructions embodied on one ormore non-transitory computer-readable media) configured to implement aninterface module 192, data gathering module 194 and processing module196 utilized by the vehicle data system 120. Furthermore, vehicle datasystem 120 may include data store 122 operable to store obtained data124, data 126 determined during operation, models 128 which may comprisea set of dealer cost model or price ratio models, or any other type ofdata associated with embodiments disclosed herein or determined duringthe implementation of those embodiments.

Vehicle data system 120 may provide a wide degree of functionality,including utilizing one or more interfaces 192 configured to, forexample, receive and respond to queries from users at computing devices110; interface with inventory companies 140, manufacturers 150, salesdata companies 160, financial institutions 182, DMVs 180 or dealers 130to obtain data; or provide data obtained, or determined, by vehicle datasystem 120 to any of inventory companies 140, manufacturers 150, salesdata companies 160, financial institutions 182, DMVs 180, external datasources 184 or dealers 130. It will be understood that the particularinterface 192 utilized in a given context may depend on thefunctionality being implemented by vehicle data system 120, the type ofnetwork 170 utilized to communicate with any particular entity, the typeof data to be obtained or presented, the time interval at which data isobtained from the entities, the types of systems utilized at the variousentities, etc. Thus, these interfaces may include, for example, webpages, web services, a data entry or database application to which datacan be entered or otherwise accessed by an operator, or almost any othertype of interface which it is desired to utilize in a particularcontext.

In general, then, using these interfaces 192 vehicle data system 120 mayobtain data from a variety of sources, including one or more ofinventory companies 140, manufacturers 150, sales data companies 160,financial institutions 182, DMVs 180, external data sources 184 ordealers 130 and store such data in data store 122. This data may be thengrouped, analyzed or otherwise processed by vehicle data system 120 todetermine desired data 126 or models 128 which are also stored in datastore 122.

A user at computing device 110 may access the vehicle data system 120through the provided interfaces 192 and specify certain parameters, suchas a desired vehicle configuration or incentive data the user wishes toapply, if any. The vehicle data system 120 can select a particular setof data in the data store 122 based on the user specified parameters,process the set of data using processing module 196 and models 128,generate interfaces using interface module 192 using the selected dataset on the computing devices 110 and data determined from theprocessing, and present these interfaces to the user at the user'scomputing device 110. Interfaces 192 may visually present the selecteddata set to the user in a highly intuitive and useful manner.

A visual interface may present at least a portion of the selected dataset as a price curve, bar chart, histogram, etc. that reflectsquantifiable prices or price ranges (e.g., “average,” “good,” “great,”“overpriced,” etc.) relative to reference pricing data points (e.g.,invoice price, MSRP, dealer cost, market average, internet average,etc.). For a detailed discussion and examples of quantifiable prices andprice ranges, readers are directed to U.S. patent application Ser. No.12/556,076, filed Sep. 9, 2009, entitled “SYSTEM AND METHOD FORAGGREGATION, ANALYSIS, PRESENTATION AND MONETIZATION OF PRICING DATA FORVEHICLES AND OTHER COMMODITIES,” and U.S. patent application Ser. No.12/556,109, filed Sep. 9, 2009, entitled “SYSTEM AND METHOD FORCALCULATING AND DISPLAYING PRICE DISTRIBUTIONS BASED ON ANALYSIS OFTRANSACTIONS,” which are fully incorporated herein by reference. Usingthese types of visual presentations may enable a user to betterunderstand the pricing data related to a specific vehicle configuration.Additionally, by presenting data corresponding to different vehicleconfigurations in a substantially identical manner, a user can easilymake comparisons between pricing data associated with different vehicleconfigurations. To further aid the understanding for a user of thepresented data, the interface may also present data related toincentives which were utilized to determine the presented data or howsuch incentives were applied to determine presented data.

Turning to the various other entities in topology 100, dealer 130 may bea retail outlet for consumer goods and/or services, such as vehiclesmanufactured by one or more of OEMs 150. Dealers 130 may be affiliatesof vehicle data system 120. To track or otherwise manage sales, finance,parts, service, inventory and back office administration needs, dealers130 may employ a dealer management system (DMS) 132. Since many DMS 132are Active Server Pages (ASP) based, transaction data 134 may beobtained by vehicle data system 120 directly from the DMS 132 with a“key” (for example, an ID and Password with set permissions within theDMS system 132) that enables data to be retrieved from the DMS system132. Many dealers 130 may also have one or more websites which may beaccessed by vehicle data system 120 over network 170, where pricing datapertinent to the dealer 130 may be presented on those websites,including any pre-determined, or upfront, pricing. This price istypically the “no haggle” price (i.e., price with no negotiation) andmay be deemed a “fair” price by vehicle data system 120.

Inventory companies 140 may be one or more inventory polling companies,inventory management companies or listing aggregators which may obtainand store inventory data from one or more of dealers 130 (for example,obtaining such data from DMS 132). Inventory polling companies aretypically commissioned by the dealer to pull data from a DMS 132 andformat the data for use on websites and by other systems. Inventorymanagement companies manually upload inventory information (photos,description, specifications) on behalf of the dealer. Listingaggregators get their data by “scraping” or “spidering” websites thatdisplay inventory content and receiving direct feeds from listingwebsites.

DMVs 180 may collectively include any type of government entity to whicha user provides data related to a vehicle. For example, when a userpurchases a vehicle it must be registered with the state (for example,DMV, Secretary of State, etc.) for tax and titling purposes. This datatypically includes vehicle attributes (for example, model year, make,model, mileage, etc.) and sales transaction prices for tax purposes, butmay not include any personally-identifiable information (PII) about thebuyer.

Financial institution 182 may be any entity such as a bank, savings andloan, credit union, etc. that provides any type of financial services toa participant involved in the purchase of a vehicle. For example, when abuyer purchases a vehicle they may utilize a loan from a financialinstitution, where the loan process usually requires two steps: applyingfor the loan and contracting the loan. These two steps may utilizevehicle and consumer information in order for the financial institutionto properly assess and understand the risk profile of the loan.Typically, both the loan application and loan agreement include proposedand actual sales prices of the vehicle though thepersonally-identifiable information about the buyer are not used by 122.Sales data companies 160 may include any entities that collect any typeof vehicle sales data. For example, syndicated sales data companiesaggregate new and used sales transaction data from DMS 132 systems ofparticular dealers 130. These companies may have formal agreements withdealers 130 that enable them to retrieve data from dealer 130 in orderto syndicate the collected data for the purposes of internal analysis orexternal purchase of the data by other data companies, dealers, andOEMs.

Manufacturers 150 can be those entities which actually build thevehicles sold by dealers 130. To guide the pricing of their vehicles,manufacturers 150 may provide an Invoice price and a Manufacturer'sSuggested Retail Price (MSRP) for both vehicles and options for thosevehicles—to be used as general guidelines for the dealer's cost andprice. These fixed prices are set by the manufacturer and may varyslightly by geographic region.

External information sources 184 may comprise any number of othervarious source, online or otherwise, which may provide other types ofdesired data, for example data regarding vehicles, pricing,demographics, economic conditions, markets, locale(s), etc.

It should be noted here that not all of the various entities depicted intopology 100 are necessary, or even desired, in embodiments disclosedherein, and that certain of the functionality described with respect tothe entities depicted in topology 100 may be combined into a singleentity or eliminated altogether. Additionally, in some embodiments,other data sources not shown in topology 100 may be utilized. Topology100 is therefore exemplary only and should in no way be taken asimposing any limitations on embodiments disclosed herein.

With the growth of internet commerce and the increasing popularity ofconsumers performing online searches for candidate dealerships beforevisiting the dealerships and perhaps making on-site purchases, there canbe increasing financial benefits for an intermediary ecommerce companyto direct consumers toward affiliated sales outlets. As an example, anoperator of vehicle data system 120 may desire to direct users atcomputing devices 110 toward dealers 130.

In this context, sales volume for an affiliate can be influenced by:

-   -   a) online display of the affiliate by an intermediary; and    -   b) the display position in which the affiliate is displayed by        the intermediary.

With this understanding, turning now to FIG. 2 which depicts adiagrammatic representation of one embodiment of system 200 implementinga methodology disclosed herein. One embodiment of vehicle data system120 described above can be an example of system 200. An ecommerceintermediary 220 may host an online market place or network site 270.Visitor 210 may visit network site 270 to search for candidatedealerships or outlets 230 a . . . 230 n for a particular vehicleconfiguration. Information such as network traffic data associated withthis visit and others alike may be gathered and stored in database 222.Other types of information may also be collected as described above. Inthis example, system 200 includes decision-making tool (also referred toherein as an affiliate decision-making or ADM tool) 290 constructed tohelp affiliates of intermediary 220 (in this example, outlets 230 a . .. 230 n) achieve target sales volume levels by affecting the level andquality of introductions to potential customers (defined in oneembodiment as the number of times a dealer is displayed). Embodiments ofan ADM tool may implement the following features:

-   1. A display position algorithm configured to determine, select, and    rank affiliates for display—the mechanics of the algorithm may vary    as long as there exists a “what-if” capability that allows a user of    the ADM tool such as a manager of an affiliate to explore, via a    user friendly graphical interface, how various decisions would    impact the resulting number of introductions.-   2. An ability for users—in the course of their business operation—to    modify some of inputs that are used by the algorithm. The ADM tool    can then present any effects of each modification and the users can    make an informed decision and can change real inputs (price,    inventory, etc.) to mirror changes made using the ADM tool (with    respect to varying the number of introductions).-   3. The display position algorithm may be triggered by an online    search at a network site owned and operated by an intermediary. To    this end, computation of the number of introductions for an    affiliate may require a known or estimated amount of demand for the    good or service for which online searches will be conducted. The ADM    tool therefore has knowledge of or can estimate:    -   a. how many times the display position algorithm will be run,        and    -   b. how many times a user will select an affiliate for display.-   4. Optionally, if the user of the ADM tool wishes to convert ranked    introductions into sales volume or gross margin metrics, the ADM    tool may provide the user:    -   a. the likelihood that an introduction will turn into a sales        lead (when a consumer engages an affiliate based on the        introduction) as a function of the display rank;    -   b. the relationship between a sales lead and the probability of        sale; and    -   c. the gross margin realized by the affiliate (not the        e-commerce intermediary) associated with each sale.

In some embodiments, the ADM tool can be used by an affiliate toinfluence the amount and position of displays of their retail outlet asa seller of the good or service being requested by an online user. Theability to understand and control the amount of known demand that willbe introduced to a seller can help optimize gross margin, throttle orexpand introductions to reflect inventory availability, build webpresence, and generate opportunities to make residual income from banneradvertisements.

As illustrated in the example of FIG. 2 , system 200 has severalinter-related components. Specifically, display position algorithm (DPA)298 is configured to compute the display order for eligible outlets thatcan result, given an online search for a good or service at network site270. Interface 292 is configured with a visualization capability thatcan translate the outcome of DPA 298 into usable metrics, includingintroductions, sales, and gross margin. Affiliate decision-making (ADM)tool 290 is configured to allow an authorized user representing aneligible outlet to interact, via interface 292, with DPA 298 to exploreways in which they may vary the number and position of the displays theywill receive given a known amount of demand (as determined based onnetwork traffic data associated with a particular vehicleconfiguration).

In some embodiments, the ADM tool may implement a so-called“pay-per-sale” (PPS) regime where an affiliate selling the good orservice pays an intermediary e-commerce company that a) maintains andoperates the DPA that generates introductions and b) is paid a per-unitrevenue by the affiliate who was introduced by the intermediary if, andonly if, a sale occurred as a result of the introduction. In oneembodiment, this distinction can be important as the DPA can be based onan objective of maximizing the revenue of the intermediary rather theaffiliate—though the two values are often highly positively correlated.In other embodiments, the DPA could be easily modified to be based on anaffiliate-centric metric or could be based on a ‘pay-per-lead’ regimewhere the outcome of the introduction (sale or no sale) isirrelevant—that is, the affiliate pays the intermediary company simplyfor the display or the resulting lead (if it follows from theintroduction). However, the modifications to address these variants areminimal. Here, for the purpose of illustration, the most complex case ispresented.

One embodiment of a methodology for varying an affiliate positiondisplayed by an intermediary will now be described with reference toFIG. 3 .

FIG. 3 depicts a diagrammatic representation of example data flow 300according to one embodiment of a method for varying an affiliateposition displayed by an intermediary. Display position algorithm (DPA)398 can be an example embodiment of DPA 298 described above. In thisexample, exogenous variables such as macro-economic data, weather, time,etc. of which an affiliate has no control are inputted into DPA 398(step 302).

If revenue maximization is the goal of an intermediary owning andoperating a network site where visitors can search for retail outletsfor items for sale, embodiments of a DPA can be configured to compute anexpected revenue for the intermediary. At this point, it can be usefulto first define the expected revenue and then further explore each ofits components. The actual amount of revenue expected to be paid to theintermediary is a function of:

P_(i,t,z): the expected probability that a customer located ingeographic unit z will purchase item t from outlet i if it is displayedto them during an online search;

π_(i,t): the per-unit revenue paid by outlet i to the e-commerceintermediary if an introduction results in a sale of item t by outlet I;and

κ: the maximum number of retail outlets that the intermediary displaysin response to the online search.

The expected revenue for the intermediary (the revenue expected to bepaid to the intermediary by outlet i), ER_(i), is then:

ER_(i, t, z) = P_(i, t, z) × π_(i, t).

At the time of the online search by a customer for item t located ingeographic unit z, an expected revenue, ER_(i,t,z), is computed for alleligible affiliates (those with whom the intermediary has an agreementto pay π_(i,t) should the introduction result in a sale) where i=1, . .. , I. After the expected revenue values are calculated, they are sortedin descending order and assigned a rank, O_(r)(i), where r=1, . . . , I.For example, the outlet i yielding the highest expected revenue amongthe cohort of eligible affiliates is assigned a rank of O₁(i), theoutlet with the next highest is expected revenue is assigned a rank ofO₂(i), and so on until all affiliates in the cohort have been assigned arank. In the event of a tie, any number of secondary sorting rules couldbe applied such as alphabetical, closest affiliate to customer,randomly, etc.

In one embodiment, a system owned and operated by an intermediary may,in response to a consumer's search request for a retail item within adefined geographical area, present to the consumer via theintermediary's network site a list of dealers of the retail item withinthe geographical area, utilizing a proprietary display positionalgorithm. One example model implementing an embodiment of a displayposition algorithm, referred to as the Dealer Scoring Algorithm (DSA),will now be described.

In this example DSA model, various types of data may be utilized,including DSA log data, drive distance data, and dealer inventory data.The DSA log data may indicate if a lead for trim tin ZIP code z (alsoreferred to as a “search zip code”) generated through the intermediary'snetwork site to any dealer, i, results in a sale. As a specific example,cohorts with leads less than 15 days old can be excluded since the leadstake time to convert into sales. Such leads may be excluded to preventunderestimate the close rate of dealers. Other temporal limitations mayalso be possible.

The drive distance and drive time of a search zip code to a dealerlocation can be obtained from online sources. Referring to FIG. 4 , as aspecific example, DSA model 400 may implement first program 410 fordecoding dealer addresses 401 via API 412 to an online geocodingservice. Outputs from first program 410 may include latitude andlongitude information 420 for dealer addresses 401. DSA model 400 mayalso implement second program 430 for determining driving distanceand/or time 450 based on various types of data, including latitude andlongitude information 420 for dealer addresses 401 and drivingdirections obtained via API 432 to an online driving directions servicesuch as mapquest.com. In the case of missing values, the drive distanceand drive time value can be imputed based on the average drive distanceand great circle distance ratio for similar nearby ZIP codes, based onzip code information from database 440.

Dealers' new car inventory information can be obtained from data feedsprovide by dealers themselves. As described above, such dealers areconsidered affiliates of the intermediary.

In addition to data types, various types of features may be consideredin the calculation of probability of closing in the DSA model. Examplefeatures will now be described.

Features describing an individual vendor (X,_(i,t)):

Common factors for these features may include price, distance from thebuyer, available inventory, services and perks, vendor reputation,historical sales performance and so on. In this example DSA model,distance can be one of the most important factors influencing buyers'decisions for large products like vehicles. In some embodiments, radialdistance can be used. In some embodiments, drive distance can be abetter indicator of the true travel distance as there may be certainareas with islands and lakes. Drive time is also introduced into the DSAmodel because the same drive distance in different locations might beassociated with a different drive time. For example, 60 miles mightrequire a 1 hour drive in a rural area but 2 hours or more in a bigcity. In some embodiments, drive time is utilized because it can beequalized to facilitate comparison across different locations.

Price can play a big role in sales in a competitive market. The priceoffset relative to the invoice price of the vehicle can be an importantfactor in the example DSA model. To reduce the price variance ofdifferent vehicles, the price offset as a percentage of invoice pricecan be used as the main price variable in the example DSA model. Wherethe worst price and best price do not differ substantially, anadditional variable that can measure the absolute difference of price asa percentage of the worst price can be used to adjust for the effect ofprice on probability of sale for those cases. For dealers who do notprovide an upfront price, the maximum price allowed by the program maxvalue can be used for their price offset.

Dealers have certain characteristics that may cause a car buyer toprefer them over others. Such characteristics may include their carinventory, special services, historical performance, and locations.Customers often complain that they are not able to get the cars theywant when they go to the dealers. Surveys indicate that vehicleunavailability can be a big cause of sale failure. It is reasonable toassume that a larger dealership is more likely to have the customer'spreferred vehicle than a smaller dealership. To this end, the exampleDSA model incorporates dealers' overall new car inventory as a variableto measure the overall dealership size. Dealers with no inventoryinformation can be assigned an average value of inventory in thecandidates' dealer list for each cohort. The comprehensiveness of theinventory can be continually improved for affiliated dealers.

The example DSA model includes additional drive distance and/or drivetime derived variables to capture the sale and distance relationship forcertain special cases. For example, it is possible that the drive timefor the closest dealer and furthest dealer does not differsubstantially. In those cases, weights on drive time can be adjusted toavoid overestimate the effect of minimum drive time on sale.

In addition to the vehicle of choice, car buyers may also consider thewarranty, maintenance and other services offered by a dealer duringtheir decision process. A system implementing the example DSA model maydisplay dealers' special services along with their upfront price andlocation in search results. Such special services may be considered as apotential factor that might influence the probability of closing a sale.For example, a “perks” dummy variable may be defined as 1 if a dealerprovides any one of a plurality of services such as limited warranty,money back guarantee, free scheduled maintenance, quality inspection,delivery, free car wash, and 0 otherwise.

In this example DSA model, probability of sale can be highly correlatedwith the historical performance of a dealer. Dealers with excellentsales people and/or good reputations should have higher close rates thanothers. Such factors can be measured by their historical close rates. Asa specific example, the DSA model can calculate the close rate for eachdealer based on their performance in the preceding 45 day window. Inthis case, a period of 45 days is chosen as the width of the windowbecause it is a medium length time window that will provide a dealer'shistorical performance, but also can quickly reflect the changes of theoverall vehicle market due to factors such as gas price change or newmodel release and so on. Equation (1) below provides for the details ofcalculation of dealer close rate. Since, in some embodiments, somedealers may only take leads from zip codes that are located within 60miles, the close rate in this example is based on the sales and leadsfrom within 60 miles of driving distance. When the close rate is missingdue to no sale or no leads in the past 45 days, the average close ratefor nearby dealers can be used. Those skilled in the art will appreciatethat the number of preceding days and the driving distance described inthis example and presented in Equation (1) are meant to be illustrativeand non-limiting and that other values may be used.

$\begin{matrix}{{{Dealer}\mspace{14mu}{close}\mspace{14mu}{rate}} = \frac{\left( {{Count}\mspace{14mu}{of}\mspace{14mu}{sales}\mspace{14mu}{in}\mspace{14mu}{last}\mspace{14mu} 45\mspace{14mu}{days}} \right)}{\left( {{Count}\mspace{14mu}{of}\mspace{14mu}{leads}\mspace{14mu}{in}\mspace{14mu}{last}\mspace{14mu} 45\mspace{14mu}{days}} \right)}} & {{Equation}\mspace{14mu}(1)}\end{matrix}$

To better predict the inventory status of a dealership and put moreweight on dealers' most recent performance, a variable “defendingchampion” is included in the example DSA model as another type ofperformance measuring variable. The defending champion variable allowsthose dealers with more recent sales be assigned a higher weight. Forinstance, dealers will get more credit for a successful sale yesterdaythan for a sale from 30 days ago. This also serves as a proxy forinventory in that the dealers who have recently made a sale for a makewill have a higher chance of having similar cars in their inventory thandealers who have not made a sale for a while.

In addition, dealer location can be very important to sales when thecustomer is located on the border of two states. Due to the differentrules on vehicle regulation and registration, people might tend to go toa dealer located in the same state as where they live. A “Same State”dummy variable is therefore included in the example DSA model toindicate if the customer and dealer are located in the same state.

Features of an Individual Vendor as Compared to Other Vendors(X_(i,t,S)):

The absolute value of an individual vendor's attributes do notnecessarily reflect its advantage or competitiveness, but they do whencompared relative to other vendors' attributes. Therefore, vendorfeatures relative to other competitors can be important factors inpredicting the probability of sale in the example DSA model.

In this example DSA model, most of the individual dealer features suchas drive time, price offset, historical close rate, inventory anddefending champing can be rescaled among all the candidate dealerswithin each cohort. For example, individual dealer's historical dealerclose rate and new car inventory variables v_(i) can be rescaled usingEquation (2) below:

$\begin{matrix}{x_{i} = \frac{\left( {v_{i -}\mspace{14mu}{\min\limits_{i}v}} \right)}{\left( {{\max\limits_{i}v} - {\min\limits_{i}v}} \right)}} & {{Equation}\mspace{14mu}(2)}\end{matrix}$

Drive time, defending champion and price can be rescaled using Equation(3) below:

$\begin{matrix}{x_{i} = {1 - \frac{\left( {v_{i -}\mspace{14mu}{\min\limits_{i}v}} \right)}{\left( {{\max\limits_{i}v} - {\min\limits_{i}v}} \right)}}} & {{Equation}\mspace{14mu}(3)}\end{matrix}$

The rescaled variables can have values between 0 and 1 such that thebest dealer in each competitive cohort gets a value of 1. For example,the dealer with the highest historical close rate gets a rescaled closerate of 1 and the dealer with lowest close rate gets a value of 0.Similarly, the dealer with the minimum drive time gets a value of 1 andthe dealer with maximum drive time gets a value of 0. Coercing thesevalues onto the same scale allows for comparison of dealers acrosscompetitive cohorts.

Features Describing an Individual Customer (Y_(c,t)):

Demographic features of individual customers can predict differentinterests in products and the likelihood of buying from a particulardealer. These may include income, family size, net worth, gender, theirdistance from the dealer, etc. Demographic data can be obtained frompublic data sources such as the U.S. census or online user databases fordifferent industries.

In the example DSA model, searched vehicle make and customer localdealer density are included in predicting the probability of buying fora particular cohort. A customer's choice of vehicle make can potentiallybe an indicator of that customer's income, family size, etc. It ishighly possible that people purchasing luxury cars are less sensitive toprice and more sensitive to drive time. To this end, the DSA model canbe configured to put more weights on distance when the customerindicates a high income zip code to increase the probability of closing.It is also reasonable to assume that price may be more important on salefor customers located in big cities with high dealer density whiledistance is more crucial for people in rural areas with only a fewdealerships available within 200 miles. A count of available dealerswithin a certain drive time radius may be used as network densityvariables. In this example, a dummy variable for each make may beincluded in the DSA model selection process using SAS proc logistic,which is known to those skilled in the art. As a specific example, threeout of 35 makes (Mercedes-Benz, Mazda, Volkswagen) have significantp-values for their dummy variables, indicating that these three makeshave different sales probability compared to other makes. Testing on themake and dealer density interaction indicates that the interactionbetween Mercedes-Benz and dealer density remains significant. Althoughthe make and network features may not affect the dealer ranks withineach cohort (each cohort can have the same make and density informationfor different candidate dealers), they may affect the expected revenuefor each dealer and the expected revenue for the intermediary.Therefore, these features are included in the example DSA model.

In the car buying industry, it can be observed that certain dealers haveoutstanding performance in certain zip code areas compared to theiraverage performance across all the zip codes. This may be due to somecustomer population characteristics in certain zip codes. For example, azip code with high density of immigrants whose first language is notEnglish might go to a dealership with sales persons who can speak theirfirst language and/or have a dealer website with their first language.Therefore, a variable measuring each dealer's performance in specificzip code is also included in the example DSA model.

Features describing historical interactions of a particular customer anda particular vendor (Y_(c,i)):

In addition to individual customers' features, their historical buyingpreferences may also influence their purchasing behavior. Examples ofhistorical buying preferences may include frequency and volume oftransactions, the price tier (low, medium high) in which theirtransactions fall, vendor's historical sales to that customer (a proxyfor loyalty), etc.

In the car buying example, it is possible that a customer might go tothe same dealer if they had purchased a car from this dealer before. Thecustomer loyalty effect might even be bigger in some other industrieswhich provide services rather than actually products. This could be oneof the most important factors for predicting the probability of buyingfor a particular customer from a certain dealer/vendor/retailer/outlet.

Operationally, the DSA may use the estimated model by feeding in thevalues of the independent variables, computing the probabilities foreach candidate dealer, and present the dealers with the topprobabilities of closing to customer c. It may consider all dealers,(i=1, . . . K) selling the same trim (t=1, . . . , T) to users in ZIPCode z (z=1, . . . , ZL) located in the same locality L (z∈L if, in oneembodiment, the great circle or radial distance from the customer'ssearch ZIP code center to dealer location 250 miles). The DSA model mayutilize a logistic regression based on the combined data of inventory,DSA logs, drive distance, and dealer perks, as illustrated in Equation(4) below:

$\begin{matrix}{P_{c} = {{f\left( {P_{s},P_{b}} \right)} = \frac{1}{1 + e^{- {({\theta_{i,t,S} + \delta_{c,t,i}})}}}}} & {{Equation}\mspace{14mu}(4)}\end{matrix}$

whereθ_(i,t,S)=β_(o)

{Features of Individual Dealer, i}

+β₁×the make of trim tis Mercedes-Benz

+β₂×Mercedes-Benz make and density interaction

+β₃× Mazda make and density interaction

+β₄× Volkswagen make and density interaction

+β₅× count of dealers within 30 min drive

+β₆×count of dealers within 1 hour drive

+β₇× count of dealers within 2 hours drive

+β₈× dealer's perks

+β₉× dealer's rescaled price within each cohort

+β₁₀×dealer's historical close rate

{Features Relative to Other Candidate Dealers, i,S}

+β₁₁× if dealer has the minimum drive time

+β₁₂× if dealer has lowest price within each cohort

+β₁₃× difference between the dealer's price and maximum price offset inpercentage of invoice; and

whereδ_(c,t,l)=α_(o)

{Features of Individual Customer, c}

+α₁×the household income of customer c

+α₂× the family size of customer c

+α₃×customer c's household size

+α₄× customer c's local dealer density

+α₅× if customer bought this type, or this make before

{Features Describing the Interaction of Customer c and Dealer i}

+α₆× distance from customer c to dealer i

+α₇× if customer c bought from dealer i before

+α₈×dealer i's rescaled number of sales in customer c's ZIP code

+α₉× if dealer i is within 10 miles of customer c

+α₁₀× if dealer i is within 10-30 miles of customer c

+α₁₁× if dealer i is within 30-60 miles of customer c

+α₁₂× if dealer i is within 60-100 miles of customer c

+α₁₃× if dealer i is within 100-250 miles of customer c

+α₁₄× if dealer i is in the same state as customer c

+α₁₅× difference between the dealer's drive time and maximum drive timewithin each cohort

+α₁₆× dealer's rescaled drive time within each cohort

+α₁₇× dealer's rescaled price and rescaled drive time interaction

+ε_(c,t,i)

Although the dealer rank may not change if customer features andcustomer historical preference variables are excluded from the DSA, theyare included in the example DSA model described above because theoverall probability of closing may be different for different makes. Theprobability of closing can be further applied to calculate each dealer'sexpected revenue and that number can be affected by the choice of makeand customer local dealer density.

Once the top three dealers are chosen, they are presented to thecustomer in the order determined by the expected revenue value.

Referring again to FIG. 3 , once the ranks have been assigned, thepresentation decision can be made and any affiliate for which the rankis less the maximum number set by the intermediary may be displayed. IfER_(i,t,z) is the expected value rank of outlet i for item tingeographic unit z, then the display decisions are:

-   -   Display outlet i if r≤κ    -   Do not display outlet i if r>κ    -   Among the κ outlets chosen for display, present in order (top to        bottom on the screen) according to rank, so the outlet for which        r=1 [indexed by O₁(i)] is the first outlet displayed, the outlet        for which r=2 is displayed below the first one, and so on until        all of the outlets with rank r=κ have been displayed.

Before explaining how an affiliate may use the ADM tool to identifyopportunities for adjusting the probability of sale (and thereby thedisplay decisions), the mechanics of that component of the expectedrevenue is first described. The per-unit revenue paid to theintermediary, π_(i,t), is likely not controllable by the affiliate.However, the display decisions (display/no display, and display order)may be influenced by the probability of sale component, P_(i,t,z), ofthe expected revenue.

As an example, the probability of outlet i closing a sale on item t to acustomer from geographic unit z can be based on a logistic regressionequation of the form:

$P_{i,t,z} = \frac{1}{1 + e^{- \theta_{i,t,z}}}$

whereθ_(i,t,z) =x _(i,t,z) γ+y _(i,t,z)β+ε_(i,t,z)=γ_(o)+γ₁ X _(i,t,z,1)+γ₂ X_(i,t,z,2)+ . . . +γ_(m) X _(i,t,z,m)+β_(q) Y _(i,t,z,q)+β_(q+1) Y_(i,t,z,q+1)+ . . . +β_(r) Y _(i,t,z,r)

-   -   each X_(i,t,z,k) (k=1, . . . , m) reflects an exogenous feature        of outlet i with respect to product t for which outlet i has no        ability to change. For example, outlet i may not have any        ability to change the distance between outlet i and the        customer. “Distance” in this case represents an exogenous        variable.    -   each Y_(i,t,z,r) (q=1, . . . , r) reflects an endogenous feature        of outlet i with respect to product t for which outlet i can        change. For example, outlet i can change the price on item t or        its customer satisfaction rating. “Price” and “customer        satisfaction rating” in this case represent endogenous        variables.

Independent variables, X and Y, reflecting 1) individual outletfeatures, 2) individual outlet features relative to other outlets, 3)individual customer features and 4) customer's historical preferenceshould be considered as potential factors based on empirical knowledgeon their relationship with closing a sale. Data transformation isperformed for variables with large variance or skewed distribution.Missing values can be imputed based on appropriate estimates such asusing local average of historical data. Forward, backward and stepwisemodel selection procedures can be used to select independent variables.Rescaled or additional derived variables can be defined in order toreduce the variance of certain variables and increase the robustness ofcoefficient estimates. The final model coefficients are chosen such thatthe resulting estimate probability of sale is most consistent with theactual observed sales actions given the vendors displayed historically.

In order to be displayed given a new search, an outlet is expectedrevenue for the intermediary, ER_(i,t,z)=P_(i,t,z)×n_(i,t), must behigher than that of outlet j indexed by O_(r)(j) such that r=n_(t,z). Asdiscussed above, π_(i,t) is unlikely to be changed by outlet i. However,outlet i can affect the display decision by modifying, for example, theprobability of sale component, P_(i,t,z), of the expected revenueequation:

$P_{i,t,z} = \frac{1}{1 + e^{- \theta_{i,t,z}}}$

whereθ_(i,t,z) =x _(i,t,z) γ+y _(i,t,z)β+ε_(i,t,z)=γ_(o)+γ₁ X _(i,t,z,1)+γ₂ X_(i,t,z,2)+ . . . +γ_(m) X _(i,t,z,m)+β_(q) Y _(i,t,z,q)+β_(q+1) Y_(i,t,z,q+1)+ . . . +β_(r) Y _(i,t,z,r)

When the revenue paid to the intermediary is identical for all i=1, . .. , I eligible affiliates, the ordered ranking Or(i) of the expectedrevenues is identical to the ordered rankings of θ_(i,t,z). Thus,changing the display position and ranking can be enabled by varying theinputs to the equation θ_(i,t,z)=x_(i,t,z)γ+y_(i,t,z)β. As the vector ofvariables x are exogenous, they may not be varied by the affiliate. By aprocess of elimination, changes in display position can be made bychanging the values of the endogenous independent variables contained inthe vector y.

As shown in FIG. 3 , in addition to the computed expected revenue forthe intermediary, DPA 398 may also output the affiliate's displayposition, number of leads, sales, and revenues for the affiliate, etc.(step 304). If output levels indicate certain target(s) is/are not met(step 306), the affiliate can, utilizing an affiliate decision-makingtool such as one embodiment of ADM tool 290 described above, determinethe level(s) of endogenous variable(s) required to meet the desiredtarget(s) (step 308).

Suppose that one of the endogenous independent variables, v_(i,t,z), canbe isolated such that y_(i,t,z)=+y′_(i,t,z)+v_(i,t,z). The value of thatvariable may be changed by outlet i in order to generate a new ranking(step 310). If the revenue paid to the ecommerce intermediary companydiffers by affiliate, the full value of the expected revenue isrecalculated by DPA 398 as the values in the linear equation, θ_(i,t,z),are varied. Again, θ_(i,t,z) is a computational component of theprobability of sale component, P_(i,t,z), which, in turn, is a componentof the expected revenue, ER_(i,t,z), for the intermediary.

One embodiment of a method for ensuring an introduction utilizing anaffiliate decision-making tool will now be described with reference toFIG. 5 .

As described above, embodiments of an ADM tool disclosed herein canallow a user to interact with a position display algorithm (PDA) to varythe value of a selected variable, v_(i,t,z), to ensure that their outletis chosen for display. As an example, in one embodiment, method 500 mayinclude determining an expected revenue for an intermediary that isassociated with outlet j in an ordered position r=κ: ER[O_(k)(j)]determined by the PDA (step 502).

Method 500 may also include determining an adjustment to an inputvariable for the PDA that outlet i must make such that the expectedrevenue for the intermediary that is associated with outlet i is thesame as outlet j (step 504). In one embodiment, this step may include:

-   -   computing the target probability of sale, P_(i,t,z) ^((k)), that        must result in order for outlet i to achieve the same expected        revenue (and rank) as outlet j who is displayed in the position        κ, where κ: P_(i,t,z) ^((k))=ER[O_(k)(j)]/π_(i,t);    -   populating the computational component, θ_(i,t,z) ^((k)) of the        target probability of sale such that θ_(i,t,z)        ^((k))=x_(i,t,z)γ+y_(i,t,z)′β−β_(v)(v_(i,t,z)+δ); and    -   solving an adjustment, δ, for a variable, v_(i,t,z), where        δ=θ_(i,t,z) ^((k))−x_(i,t,z)γ−y_(i,t,z)′β−_(v)v_(i,t,z).

The value of the input variable, v_(i,t,z), can then be adjusted suchthat the display position for outlet i is ranked higher than displayposition κ for outlet j (step 506). In one embodiment, this can be doneby first adjusting the value of the input variable, v_(i,t,z), by anamount equal to (α×δ) where α=−1 if β_(v)≤0 and α=+1 if β_(v)>0. Thisensures that the final ER value (for the intermediary) associated withoutlet i is the same as the ER associated with outlet j (and so thedecision by the intermediary to display is the same for both outlets).The adjustment, δ, can then be modified by a tiny amount, ε, untilO_(κ-1)(h)<O_(κ)(i,δ+ε)<O_(κ)(j), where O_(κ-1)(h) corresponds to outleth with rank κ−1 prior to the changes induced by δ. This ensures outlet ia chance to be displayed and introduced by the intermediary.

One embodiment of a method for varying display position to any rank, s,will now be described with reference to FIG. 6 .

As discussed above, an affiliate may make decisions to ensure that theiroutlet is displayed and receive an introduction when a consumer searchesfor an item that the outlet has for sale. Method 500 illustrates anexample by which an affiliate can not only ensure that their outlet bedisplayed, but may also achieve a specific position in the ordereddisplay.

As an example, in one embodiment, method 600 may include determining anexpected revenue for an intermediary that is associated with outlet j inan ordered position r=s: ER[O_(s)(j)] determined by the PDA (step 602).

Method 600 may also include determining an adjustment to an inputvariable for the PDA that outlet i must make such that the expectedrevenue for the intermediary that is associated with outlet i is thesame as outlet j (step 604). In one embodiment, this step may include:

-   -   computing the target probability of sale, P_(i,t,z) ^((s)), that        must result in order for outlet i to achieve the same expected        revenue (and rank) as outlet j who is displayed in the position        s, where s: P_(i,t,z) ^((s))=ER[O_(s)(j)]/π_(i,t);    -   populating the computational component, θ_(i,t,z) ^((s)), of the        target probability of sale such that θ_(i,t,z)        ^((s))=x_(i,t,z)γ+y_(i,t,z)′β−β_(v)(v_(i,t,z)+δ); and    -   solving an adjustment, δ, for a variable, v_(i,t,z), where        δ=θ_(i,t,z) ^((s))−x_(i,t,z)γ−y_(i,t,z)′β−β_(v)v_(i,t,z).

The value of the input variable, v_(i,t,z), can then be adjusted suchthat outlet i is ranked at a specific display position (step 606). Inone embodiment, this can be done by first adjusting the value of theinput variable, v_(i,t,z), by an amount equal to (α×δ) where α=−1 ifβ_(v)≤0 and α=+1 if β_(v)>0. This ensures that the final ER value (forthe intermediary) associated with outlet i is the same as the ERassociated with outlet j (and so the decision by the intermediary todisplay is the same for both outlets). The adjustment, δ, can then bemodified by a tiny amount, ε. If the rank before attempting toreposition outlet i is denoted O_(b)(i) and:

-   -   If b<s then choose an adjustment value target so that        O_(s−1)(h)<O_(s)(i,δ+ε)<O_(s)(j), where O_(s−1)(h) corresponds        to outlet h with rank s−1 prior to the changes induced by δ.    -   If b>s then choose an adjustment value target so that        O_(s)(j)>O_(s)(i,δ+ε)>O_(s+1)(h) where O_(s+1)(h) corresponds to        outlet h with rank s+1 prior to the changes induced by δ.

By analyzing historical data, one can determine the percentage ofintroductions that convert in a lead λ_(i,t,z). If n_(i,t,z)(r)represents the number of historical introductions by in geographic unitz for item t and outlet i and λ_(i,t,z)(r) represents the number ofleads that were resulted from those introductions, then the conversionrate for each display positions can be:c_(i,t,z)(r)=λ_(i,t,z)(r)÷n_(i,t,z)(r). Based on expected future demand,d_(t,z), for item tin area z, the total number of expected leads is:

$L_{i,t,z} = {\sum\limits_{r = 1}^{k}\;{{n_{i,t,z}\left( {{r\text{:}r} \leq k} \right)} \times {c_{i,t,z}(r)}}}$

where n_(i,t,z)(r: r≤k) are the introductions in position r thatoccurred because the expected revenue value was high enough to cause theaffiliate to be displayed.

If an affiliate is working under a pay-per-lead agreement with theintermediary, they may employ the ADM tool discussed above to determinehow many leads they would be responsible for paying, along with themonetary value associated with the service provided by the intermediary.Should the affiliate desire to go one step further and see how theirdisplay position ranking decisions will impact sales volume and revenuefor the affiliate, the ADM tool can also make that possible. First, afew more definitions:

-   ρ_(i,t): represents the per-unit revenue realized by an affiliate    when the sale of item tis made.-   χ_(i,t): represents the per-unit cost to the affiliate for item t.-   L_(i,t,z): represents the number of leads for goods/services of item    t offered by outlet i to geographic unit z.-   ω_(i,t): represents the amount of inventory of item t for which    outlet i can sell.

The geographic unit z may be a ZIP code, city, county, state, or anyother spatial entity for which the online search was restricted.

Computation of the expected number of sales is:

Sales_(i,z)=Σ_(t=1) ^(T)min(ω_(i,t),Σ_(z=1) ^(Z)P_(i,t,z)L_(i,t,z)) andthe total amount of gross margin is: GM_(i)=[Σ_(t=1)^(T)(ρ_(i,t)−c_(i,t))·min(ω_(i,t)Σ_(z=1) ^(Z)P_(i,t,z)L_(i,t,z))]. Notein this case, the total sales of item t is constrained by the availableinventory at outlet i.

The ability to easily visualize the effects of varying inputs on variousmetrics such as display rank, leads, sales, and gross margin can providemany advantages. For example, embodiments of an affiliatedecision-making tool can allow a customer or affiliate of anintermediary to:

-   -   Know how many introductions are anticipated in a specified time        period (e.g., the next fiscal quarter) in a specific geographic        area for all of the affiliates (themselves and their        competitors) offering the product being request.    -   Understand, assuming existing levels of business inputs        (inventory, prices, etc.), the number and percentage of        introductions they will receive in that time period and the        display positions of each of their introductions.    -   Develop an estimate of how many leads and sales are expected to        result from their present operations in the local competitive        market along with the gross margins associated with that        activity.    -   Develop an ability to select any endogenous business input over        which he has control and determine how varying their values will        impact introductions, display positions, leads, sales, and gross        margin.        -   A user may explore varying levels of each endogenous            variable one-by-one while holding fixed all other inputs,            including competitors ER values, exogenous variables, and            the endogenous variables not being presently analyzed.        -   A user may find an optimal value of one endogenous input,            update its value, and explore a different endogenous            variable.

FIGS. 7A-7C illustrate how effects of varying inputs on various metricsmay be visualized. Suppose an affiliate is associated with an outletidentification (outlet ID) ‘123’ and is researching a particular item‘XYZ’ in a local area ‘California.’ Additionally, suppose an ecommerceintermediary will only show the top κ=3 affiliates and demand analysisindicates that there should be 300 total introductions made in theperiod of analysis. FIG. 7A shows that a user has chosen an endogenousvariable ‘Variable 2’ having a value ‘200’ for further analysis. FIG. 7Bshows all the possible values of Variable 2 and the number ofintroductions, leads, sales, and revenue associated therewith. In thisexample, Variable 2 having a value 200 is also associated with the thirddisplay position that the ecommerce intermediary will show to a websitevisitor inquiring about item XYZ in California. Embodiments of an ADMtool can have knowledge of all existing values for all input variablesand be cognizant of the inputs for all of affiliate 123's competitorsoffering item XYZ in California. FIG. 7B also shows that affiliate 123has set the level of Variable 2 at 200 and has chosen that endogenousvariable for exploration. FIG. 7C depicts a plot diagram illustratingeffects of setting Variable 2 at various levels on the number of sales(701), the number of introductions (703), and/or the number of leads(705). As FIGS. 7A-7C exemplify, these visualizations can help a userunderstand how changing the level of Variable 2 to different values(between 0 and 1000) may impact display position, leads, sales and grossmargin for affiliate 123.

An example embodiment in the context of the retail automotive industrywill now be described in detail. Specifically, if an ecommerceintermediary (such as one embodiment of intermediary 220 describedabove) has an agreement to make introductions between individualssearching for a vehicle on their site (such as one embodiment of networksite 270 described above) and dealerships (such as one embodiment ofoutlets 230 a . . . outlets 230 n described above) offering the vehiclebeing considered, the decision-making tool (such as one embodiment ofaffiliate decision-making tool 290 described above) would beappropriate. Dealers could use the decision-making tool to understandhow changing its endogenous variables (e.g., vehicle prices, itsinventory, customer satisfactions scores, customer perks, etc.) wouldaffect the number of introductions, leads, sales and gross margin. Inthis example, a system (such as one embodiment of vehicle data system200 described above) that is owned and operated by the intermediary mayimplement a display position algorithm (such as DPA 298 describedabove). The intermediary may operate in a known competitive dealerenvironment in which the system would have knowledge with respect to thenumber of dealers (affiliates), dealer locations, vehicle pricing, etc.In response to an online search by a visitor to the network site (suchas visitor 210 to network site 270 described above), the system maydetermine which dealers should be introduced to the user and in whatorder should the eligible dealers be presented. In some embodiments, theintermediary can be paid a flat fee for every introduction that yields asale. Since an objective of the intermediary and its affiliates are tomaximize sales, all parties involved can realize a benefit (increasedrevenue) when a sale is made. In some embodiments, the Expected Revenuevalue (ER) used during execution of the example display positionalgorithm disclosed herein can be based on the amount of revenueexpected to be paid to the intermediary.

One example of how a dealer affiliated with the intermediary can benefitfrom an embodiment of an affiliate decision-making (ADM) tool disclosedherein is that the ADM tool can allow the dealer to investigate how theendogenous price variable may be used to influence display position andthe number of leads. The ADM tool can assist dealers in optimizing theirprice setting and inventory selection. As the display position algorithm(DPA) can be a function of the dealer's price, the dealer could solvefor various levels of price in closed-form to identify the levels ofprice at which the dealer's expected revenue will place it in the topdisplay positions κ (where κ=3 in one embodiment) from among thepossible selection group.

As a specific example, the system may operate to perform the following:

1): Using demand from the last 30 days, re-estimate ER for each vehicletrim the system received at the price submitted. Referring to FIG. 8 ,which depicts an example of a view where the dealer-level ER for variousMake/Model/Trims in various customer ZIP codes are displayed. The‘offset price’ is an expressed value relative to the dealer invoiceamount. As an example, an offset or −250 means the offered price is $250below invoice. ‘Rank at time’ may indicate the display rank computed bythe DPA and ‘currently displayed’ may indicate if an introduction wasmade (when the maximum number of display positions is 3 and thedetermined display rank is less than or equal to 3).

2): The system may compute what level of pricing would be necessary forthis dealer to display. This computation can be performed using theprocedure described above. By selecting a price that would increase theER by an amount such that it is equal to the ER of the competitorcurrently in the third display position, the dealer can slightly lowerits price so that its ER is sufficient to be the third ranked dealer.This is illustrated in FIG. 8 where the system computes and presents toa user (for an affiliated dealership) the price of the vehicle that mustbe offered in order for the dealership to be displayed (being in thethird position) or not displayed (being in the fourth position).

3): The system may aggregate this information into a dealer-trim levelto provide the estimated number of leads for each dealership, asillustrated in FIG. 9 . In this case, it can be seen that the greaterthe offset the higher the number of leads at both the trim level and themodel level for this particular dealer. As illustrated in FIG. 10 , thisinformation can be graphically displayed to show the dealer how theirvehicle pricing may affect the number of leads that they may receivefrom the intermediary.

4): The system may estimate relative demand levels for a period of time.As an example, this can be the next 30 days. This forecast can beperformed based on expert opinion or any typical forecastingmethodology. For instance, the forecast might determine that demand overthe next 30 days is likely to be 10% higher than that over the past 30days. This factor can be used to scale up the historically known demanddescribed above. Note that a small number of examples is illustratedherein. Thus, when apply a scaling factor like 1.1, there might be manyfractional values. In practice, with a very large sample, these factorsmay be large enough to make a difference while still rounding off to thenearest whole number.

FIG. 11 depicts an example interface of a vehicle data systemimplementing one embodiment of a methodology disclosed herein.Specifically, in this example, interface 1100 may represent a portion ofan automotive website owned and operated by TrueCar, Inc. (“TrueCarwebsite”). As demand for vehicles is realized through searches on theTrueCar website, embodiments implementing a display position algorithmdescribed above can determine which dealers are displayed and the orderin which they are displayed. In this way, the TrueCar website canintroduce their affiliates to website visitors. The number of affiliatesdisplayed by the TrueCar website to a website visitor is referred to asthe number of introductions. In the example of FIG. 11 , threeintroductions—introductions 1101, 1103, and 1105 for three affiliateddealers in the search zip code ‘90401’—are made and shown to a websitevisitor via interface 1100. At this point, although a quantity ofintroductions has occurred, no lead has been generated. Should thewebsite visitor choose to proceed, they can select any number of theaffiliates thus introduced. In this example, two affiliates associatedwith introductions 1101 and 1105 are selected. In FIG. 11 ,introductions 1101, 1103, and 1105 do not show the names of theaffiliates. In some embodiments, the name of an affiliate may beincluded in an introduction.

To determine varying levels of estimated introductions, embodiments canvary the price to identify a functional relationship between price andthe quantity of introductions. For example, the display positionalgorithm can construct an ‘expected revenue’ value reflecting theprobability of sales and the revenue that would benefit TrueCar if thesale is realized.

In some embodiments, the expected revenue can be computed with respectto TrueCar to reflect a pay-per-sale model (PPS) where TrueCar is paidonly when an introduction they make on a dealer's behalf is eligible tobe displayed to a consumer (for instance, via interface 1100) in orderof expected revenue (highest expected revenue on top, followed by thesecond highest expected revenue). As an example, in some embodiments, tobe eligible for display, the dealer must be an approved member of theTrueCar network, be within close geographic proximity of the customer(no more than 3 hours driving time in one embodiment), and sell theautomotive brand that corresponds to the customer's query.

In the example of FIG. 11 , the number of dealers displayed is limitedto 3 (if that many dealers are eligible). In this case, to be eligiblefor display, the dealer would have one of the three highest expectedrevenue values among eligible dealers. As selection of display and thecorresponding position are desirable (the first display position hashistorically been highly correlated with sales volume/gross margin),network dealers are interested in taking action that will change theirdisplay position. The innovation presented in this disclosure can helpdealers determine how changes in pricing, performance, inventorymanagement, and other factors can be used to affect desired positionsdisplayed via an intermediary, such as the TrueCar website owned andoperated by TrueCar, and thereby achieve gross margin targets.

FIG. 12 depicts example leads generated from the introductions shown inFIG. 11 , according to one embodiment disclosed herein. As shown in FIG.11 , only two of the three introductions were of interest to the websitevisitor (as selected by the website visitor via interface 1100).Consequently, only two leads were generated from the three introductionsmade by the intermediary. In this example, the website visitor ispresented with, via interface 1200, location and contact information foreach of the selected dealers 1201 and 1205, along with the specificvehicle configuration and vehicle pricing information. The competitive,upfront price quotes from dealers 1201 and 1205 may be presented in formof certificates which the website visitor can take to dealers 1201and/or 1205 and perhaps make an on-site purchase of the specific vehicleconfiguration, thereby closing a sale.

In embodiments described above, a user can maneuver an affiliatedecision-making tool and explore the impact on displays, leads, sales,and gross margin that may result from setting endogenous variables atvarious levels. Some embodiments of an affiliate decision-making toolmay be configured to operate in an automated manner. More specifically,a user can set a single desired level of output (displays or leads orsales or gross margin), and the affiliate decision-making tool wouldautomatically determine a set of values for the full set of endogenousvalues that would result in the target level of output. The user mayalso hold the level of any endogenous variable fixed and the affiliatedecision-making tool could then determine a set of levels for which theremaining endogenous variables would yield the desired solution, if sucha solution exists. In such automated embodiments, a user could set thedesired output levels and no longer need to interact with the affiliatedecision-making tool. Accordingly, the user would be allowed to “set itand forget it”.

Although the invention has been described with respect to specificembodiments thereof, these embodiments are merely illustrative, and notrestrictive of the invention. The description herein of illustratedembodiments of the invention, including the description in the Abstractand Summary, is not intended to be exhaustive or to limit the inventionto the precise forms disclosed herein (and in particular, the inclusionof any particular embodiment, feature or function within the Abstract orSummary is not intended to limit the scope of the invention to suchembodiment, feature or function). Rather, the description is intended todescribe illustrative embodiments, features and functions in order toprovide a person of ordinary skill in the art context to understand theinvention without limiting the invention to any particularly describedembodiment, feature or function, including any such embodiment featureor function described in the Abstract or Summary. While specificembodiments of, and examples for, the invention are described herein forillustrative purposes only, various equivalent modifications arepossible within the spirit and scope of the invention, as those skilledin the relevant art will recognize and appreciate. As indicated, thesemodifications may be made to the invention in light of the foregoingdescription of illustrated embodiments of the invention and are to beincluded within the spirit and scope of the invention. Thus, while theinvention has been described herein with reference to particularembodiments thereof, a latitude of modification, various changes andsubstitutions are intended in the foregoing disclosures, and it will beappreciated that in some instances some features of embodiments of theinvention will be employed without a corresponding use of other featureswithout departing from the scope and spirit of the invention as setforth. Therefore, many modifications may be made to adapt a particularsituation or material to the essential scope and spirit of theinvention.

Reference throughout this specification to “one embodiment,” “anembodiment,” or “a specific embodiment” or similar terminology meansthat a particular feature, structure, or characteristic described inconnection with the embodiment is included in at least one embodimentand may not necessarily be present in all embodiments. Thus, respectiveappearances of the phrases “in one embodiment,” “in an embodiment,” or“in a specific embodiment” or similar terminology in various placesthroughout this specification are not necessarily referring to the sameembodiment. Furthermore, the particular features, structures, orcharacteristics of any particular embodiment may be combined in anysuitable manner with one or more other embodiments. It is to beunderstood that other variations and modifications of the embodimentsdescribed and illustrated herein are possible in light of the teachingsherein and are to be considered as part of the spirit and scope of theinvention.

In the description herein, numerous specific details are provided, suchas examples of components and/or methods, to provide a thoroughunderstanding of embodiments of the invention. One skilled in therelevant art will recognize, however, that an embodiment may be able tobe practiced without one or more of the specific details, or with otherapparatus, systems, assemblies, methods, components, materials, parts,and/or the like. In other instances, well-known structures, components,systems, materials, or operations are not specifically shown ordescribed in detail to avoid obscuring aspects of embodiments of theinvention. While the invention may be illustrated by using a particularembodiment, this is not and does not limit the invention to anyparticular embodiment and a person of ordinary skill in the art willrecognize that additional embodiments are readily understandable and area part of this invention.

Embodiments of the present invention can be implemented in a computercommunicatively coupled to a network (for example, the Internet, anintranet, an internet, a wide area network (WAN), a local area network(LAN), a storage area network (SAN), etc.), another computer, or in astandalone computer. As is known to those skilled in the art, thecomputer can include a central processing unit (“CPU”) or processor, atleast one read-only memory (“ROM”), at least one random access memory(“RAM”), at least one hard drive (“HD”), and one or more input/output(“I/O”) device(s). The I/O devices can include a keyboard, monitor,printer, electronic pointing device (for example, mouse, trackball,stylus, etc.), or the like. In embodiments of the invention, thecomputer has access to at least one database over the network.

ROM, RAM, and HD are computer memories for storing computer-executableinstructions executable by the CPU or capable of being compiled orinterpreted to be executable by the CPU. Within this disclosure, theterm “computer-readable medium” is not limited to ROM, RAM, and HD andcan include any type of data storage medium that can be read by aprocessor. For example, a computer-readable medium may refer to a datacartridge, a data backup magnetic tape, a floppy diskette, a flashmemory drive, an optical data storage drive, a CD-ROM, ROM, RAM, HD, orthe like. The processes described herein may be implemented in suitablecomputer-executable instructions that may reside on a computer-readablemedium (for example, a disk, CD-ROM, a memory, etc.). Alternatively, thecomputer-executable instructions may be stored as software codecomponents on a DASD array, magnetic tape, floppy diskette, opticalstorage device, or other appropriate computer-readable medium or storagedevice.

Any suitable programming language can be used to implement the routines,methods or programs of embodiments of the invention described herein,including C, C++, Java, JavaScript, HTML, or any other programming orscripting code, etc. Other software/hardware/network architectures maybe used. For example, the functions of the disclosed embodiments may beimplemented on one computer or shared/distributed among two or morecomputers in or across a network. Communications between computersimplementing embodiments can be accomplished using any electronic,optical, radio frequency signals, or other suitable methods and tools ofcommunication in compliance with known network protocols.

Different programming techniques can be employed such as procedural orobject oriented. Any particular routine can execute on a single computerprocessing device or multiple computer processing devices, a singlecomputer processor or multiple computer processors. Data may be storedin a single storage medium or distributed through multiple storagemediums, and may reside in a single database or multiple databases (orother data storage techniques). Although the steps, operations, orcomputations may be presented in a specific order, this order may bechanged in different embodiments. In some embodiments, to the extentmultiple steps are shown as sequential in this specification, somecombination of such steps in alternative embodiments may be performed atthe same time. The sequence of operations described herein can beinterrupted, suspended, or otherwise controlled by another process, suchas an operating system, kernel, etc. The routines can operate in anoperating system environment or as stand-alone routines. Functions,routines, methods, steps and operations described herein can beperformed in hardware, software, firmware or any combination thereof.

Embodiments described herein can be implemented in the form of controllogic in software or hardware or a combination of both. The controllogic may be stored in an information storage medium, such as acomputer-readable medium, as a plurality of instructions adapted todirect an information processing device to perform a set of stepsdisclosed in the various embodiments.

It is also within the spirit and scope of the invention to implement insoftware programming or of the steps, operations, methods, routines orportions thereof described herein, where such software programming orcode can be stored in a computer-readable medium and can be operated onby a processor to permit a computer to perform any of the steps,operations, methods, routines or portions thereof described herein. Theinvention may be implemented by using software programming or code inone or more general purpose digital computers, by using applicationspecific integrated circuits, programmable logic devices, fieldprogrammable gate arrays, optical, chemical, biological, quantum ornanoengineered systems, components and mechanisms may be used. Ingeneral, the functions of the invention can be achieved by any means asis known in the art. For example, distributed, or networked systems,components and circuits can be used. In another example, communicationor transfer (or otherwise moving from one place to another) of data maybe wired, wireless, or by any other means. Based on the disclosure andteachings provided herein, a person of ordinary skill in the art willappreciate other ways and/or methods to implement the invention.

A “computer-readable medium” may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, system ordevice. The computer-readable medium can be, by way of example only butnot by limitation, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, system, device,propagation medium, or computer memory. Such computer-readable mediumshall generally be machine readable and include software programming orcode that can be human readable (e.g., source code) or machine readable(e.g., object code). Examples of non-transitory computer-readable mediacan include random access memories, read-only memories, hard drives,data cartridges, magnetic tapes, floppy diskettes, flash memory drives,optical data storage devices, compact-disc read-only memories, and otherappropriate computer memories and data storage devices. In anillustrative embodiment, some or all of the software components mayreside on a single server computer or on any combination of separateserver computers. As one skilled in the art can appreciate, a computerprogram product implementing an embodiment disclosed herein may compriseone or more non-transitory computer-readable media storing computerinstructions translatable by one or more processors in a computingenvironment.

A “processor” includes any, hardware system, mechanism or component thatprocesses data, signals or other information. A processor can include asystem with a general-purpose central processing unit, multipleprocessing units, dedicated circuitry for achieving functionality, orother systems. Processing need not be limited to a geographic location,or have temporal limitations. For example, a processor can perform itsfunctions in “real-time,” “offline,” in a “batch mode,” etc. Portions ofprocessing can be performed at different times and at differentlocations, by different (or the same) processing systems.

It will also be appreciated that one or more of the elements depicted inthe drawings/figures can also be implemented in a more separated orintegrated manner, or even removed or rendered as inoperable in certaincases, as is useful in accordance with a particular application.Additionally, any signal arrows in the drawings/figures should beconsidered only as exemplary, and not limiting, unless otherwisespecifically noted.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,product, article, or apparatus that comprises a list of elements is notnecessarily limited only those elements but may include other elementsnot expressly listed or inherent to such process, product, article, orapparatus.

Further, unless expressly stated to the contrary, “or” refers to aninclusive or and not to an exclusive or. For example, a condition A or Bis satisfied by any one of the following: A is true (or present) and Bis false (or not present), A is false (or not present) and B is true (orpresent), and both A and B are true (or present). As used herein,including the claims that follow, a term preceded by “a” or “an” (and“the” when antecedent basis is “a” or “an”) includes both singular andplural of such term, unless clearly indicated within the claim otherwise(i.e., that the reference “a” or “an” clearly indicates only thesingular or only the plural). Also, as used in the description hereinand throughout the claims that follow, the meaning of “in” includes “in”and “on” unless the context clearly dictates otherwise. The scope of thepresent disclosure should be determined by the following claims andtheir legal equivalents.

What is claimed is:
 1. A method for varying a display position of adealer on a website, the method comprising: receiving, by a servercomputer, exogenous variables associated with an item, each of theexogenous variables representing an exogenous feature of a dealer withrespect to the item, the exogenous feature not under control by thedealer; providing, by a server computer through a user interface on auser device under control of the dealer, a view comprising endogenousvariables and the exogenous variables associated with the item, each ofthe endogenous variables representing an endogenous feature of thedealer with respect to the item, each of the endogenous featureschangeable by the dealer, the server computer associated with thewebsite and operating independently of the dealer, wherein the userinterface on the user device includes a visualization function foruser-selection of any of the endogenous variables in the view and forvisualizing effects of varying a value of a user-selected endogenousvariable; receiving, by the server computer through the user interfaceon the user device, an indication that an endogenous variable has beenselected by a user from the endogenous variables in the view for furtheranalysis, the user-selected endogenous variable having a value set bythe dealer, the value associated with a display position for the dealerthat the website is to display to a website visitor inquiring about theitem through the website; in response to the indication, computing, bythe server computer, effects of how setting the user-selected endogenousvariable at different levels affects a plurality of metrics that theserver computer provides to the dealer through the website; generating,by the server computer, a visualization of the effects of setting theuser-selected endogenous variable at the different levels, wherein thevisualization of the effects comprises the plurality of metrics, andwherein the plurality of metrics relate to factors that affect potentialfuture sales of items by the dealer; and providing, through the userinterface on the user device, the visualization of the effects ofsetting the user-selected endogenous variable at the different levels.2. The method according to claim 1, wherein the plurality of metricsincludes the display positions, the number of introductions, the numberof leads, and the number of expected sales of the item.
 3. The methodaccording to claim 1, further comprising: in response to the websitevisitor inquiring about the item in a geographic unit through thewebsite, determining eligible dealers in the geographic unit and, foreach respective dealer of the eligible dealers in the geographic unit,determining an expected revenue that the respective dealer is to pay anintermediary; sorting the eligible dealers in the geographic unit basedon the expected revenue determined for the respective dealer; inaccordance with the sorting, assigning a rank to the respective dealerof the eligible dealers in the geographic unit; selecting a set ofdealers from the eligible dealers in the geographic unit based on therank assigned to the respective dealer; determining display positions ofthe set of dealers on the website based on the rank assigned to therespective dealer; and presenting the set of dealers in the geographicunit to the website visitor through the website according to the displaypositions thus determined.
 4. The method according to claim 1, whereinthe endogenous feature of the dealer with respect to the item comprisesa dealer price for the item, a customer satisfaction rating, a dealerinventory, a customer perk, or a dealer-provided incentive.
 5. Themethod according to claim 1, wherein the exogenous feature of the dealerwith respect to the item comprises a distance between the dealer and thewebsite visitor, macroeconomic data, weather, or time.
 6. The methodaccording to claim 1, wherein the visualization of the effects comprisesa data structure, table, or graph.
 7. The method according to claim 1,wherein the item comprises a vehicle having a vehicle configuration andresiding in a geographic unit.
 8. The method according to claim 1,further comprising: determining an adjustment to the value of theuser-selected endogenous variable such that the display position for thedealer is within a maximum number of display positions that the websiteis to display to a website visitor inquiring about the item through thewebsite.
 9. The method according to claim 1, further comprising:determining an adjustment to the value of the user-selected endogenousvariable such that the display position for the dealer is at a specificposition that the website is to display to a website visitor inquiringabout the item through the website.
 10. The method according to claim 1,further comprising: providing, by the server computer through the userinterface on the user device, a dealer decision-making tool having avisualization function for generating the visualization.
 11. A systemfor varying a display position of a dealer on a website, the systemcomprising: a processor; a non-transitory computer-readable medium; andstored instructions translatable by the processor for: receivingexogenous variables associated with an item, each of the exogenousvariables representing an exogenous feature of a dealer with respect tothe item, the exogenous feature not under control by the dealer;providing, through a user interface on a user device, a view comprisingendogenous variables and the exogenous variables associated with theitem, each of the endogenous variables representing an endogenousfeature of the dealer with respect to the item, each of the endogenousfeatures changeable by the dealer, the server computer associated withthe website and operating independently of the dealer, wherein the userinterface on the user device includes a visualization function foruser-selection of any of the endogenous variables in the view and forvisualizing effects of varying a value of a user-selected endogenousvariable; receiving, through the user interface on the user device, anindication that an endogenous variable has been selected by a user fromthe endogenous variables in the view for further analysis, theuser-selected endogenous variable having a value set by the dealer, thevalue associated with a display position for the dealer that the websiteis to display to a website visitor inquiring about the item through thewebsite; in response to the indication, computing effects of how settingthe user-selected endogenous variable at different levels affects aplurality of metrics that the server computer provides to the dealerthrough the website; generating a visualization of the effects ofsetting the user-selected endogenous variable at the different levels,wherein the visualization of the effects comprises the plurality ofmetrics, and wherein the plurality of metrics relate to factors thataffect potential future sales of items by the dealer; and providing,through the user interface on the user device, the visualization of theeffects of setting the user-selected endogenous variable at thedifferent levels.
 12. The system of claim 11, wherein the plurality ofmetrics includes the display positions, the number of introductions, thenumber of leads, and the number of expected sales of the item.
 13. Thesystem of claim 11, wherein the stored instructions are furthertranslatable by the processor for: in response to the website visitorinquiring about the item in a geographic unit through the website,determining eligible dealers in the geographic unit and, for eachrespective dealer of the eligible dealers in the geographic unit,determining an expected revenue that the respective dealer is to pay anintermediary; sorting the eligible dealers in the geographic unit basedon the expected revenue determined for the respective dealer; inaccordance with the sorting, assigning a rank to the respective dealerof the eligible dealers in the geographic unit; selecting a set ofdealers from the eligible dealers in the geographic unit based on therank assigned to the respective dealer; determining, display positionsof the set of dealers on the website based on the rank assigned to therespective dealer; and presenting the set of dealers in the geographicunit to the website visitor through the website according to the displaypositions thus determined.
 14. The system of claim 11, wherein theendogenous feature of the dealer with respect to the item comprises adealer price for the item, a customer satisfaction rating, a dealerinventory, a customer perk, or a dealer-provided incentive.
 15. Thesystem of claim 11, wherein the exogenous feature of the dealer withrespect to the item comprises a distance between the dealer and thewebsite visitor, macroeconomic data, weather, or time.
 16. The system ofclaim 11, wherein the visualization of the effects comprises a datastructure, table, or graph.
 17. The system of claim 11, wherein the itemcomprises a vehicle having a vehicle configuration and residing in ageographic unit.
 18. The system of claim 11, wherein the storedinstructions are further translatable by the processor for: determiningan adjustment to the value of the user-selected endogenous variable suchthat the display position for the dealer is within a maximum number ofdisplay positions that the website is to display to a website visitorinquiring about the item through the website.
 19. The system of claim11, wherein the stored instructions are further translatable by theprocessor for: determining an adjustment to the value of theuser-selected endogenous variable such that the display position for thedealer is at a specific position that the website is to display to awebsite visitor inquiring about the item through the website.
 20. Thesystem of claim 11, wherein the stored instructions are furthertranslatable by the processor for: providing, through the user interfaceon the user device, a dealer decision-making tool having a visualizationfunction for generating the visualization.